Inter-metric correlations are shown in Figure REF  for MNIST, CIFAR-10 and ImageNet (results on the other datasets were similar).
In general, we note similar patterns of correlations for the three datasets. Most metrics have relatively low correlations, suggesting that they might be measuring different underlying aspects of the attribution maps, as proposed in [1]}. We also note strong negative correlations between certain pairs of metrics, more specifically MoRF/LeRF-pairs, which suggests that MoRF/LeRF-pairs contain largely redundant information. This insight can be used to reduce computational cost in future benchmarking efforts, by selecting only MoRF or LeRF metrics. Finally, we note that correlations between segmented and non-segmented metrics (for example, Deletion and IROF) are stronger for low-dimensional datasets. This is to be expected, since the low dimensionality of the data causes segments to be composed only of a few pixels.
